TabReason: A Reinforcement Learning-Enhanced Reasoning LLM for Explainable Tabular Data Prediction
This addresses the need for explainable AI in real-world applications like finance, though it is incremental as it builds on existing LLM and reinforcement learning techniques.
The paper tackles the problem of low interpretability in tabular data prediction by proposing a reinforcement learning-enhanced LLM that improves accuracy and generates human-understandable explanations, achieving better performance on financial benchmarks compared to established LLMs.
Predictive modeling on tabular data is the cornerstone of many real-world applications. Although gradient boosting machines and some recent deep models achieve strong performance on tabular data, they often lack interpretability. On the other hand, large language models (LLMs) have demonstrated powerful capabilities to generate human-like reasoning and explanations, but remain under-performed for tabular data prediction. In this paper, we propose a new approach that leverages reasoning-based LLMs, trained using reinforcement learning, to perform more accurate and explainable predictions on tabular data. Our method introduces custom reward functions that guide the model not only toward better prediction accuracy but also toward human-understandable reasons for its predictions. The proposed method is evaluated on financial benchmark datasets and compared against established LLMs.